System, Apparatus and Method for Mapping

ABSTRACT

The present invention provides a novel apparatus and method for mapping of urban regions. An apparatus includes the remote sensing equipment that is connected to a computer processor. The remote sensing equipment gathers imaging data about an urban region. The computer processor interprets the imaging data to generate a map of the urban region comprising representations that identify a first set of indicia representing physiographic characteristics, a second set of indicia representing different types of built forms, and a third set of indicia representing patterns of human activity associated with both the physiographic characteristics and the built forms. The map can also include a fourth set of indicia representing an intensity level that at least one of the other types of indicia occurs.

PRIORITY CLAIM

The present application is a continuation of PCT Patent ApplicationNumber PCT/CA2004/002143, filed on Dec. 16, 2004, which claims priorityfrom U.S. Provisional Patent Application No. 60/530,283, filed on Dec.18, 2003, the contents of which are incorporated herein by reference.

FIELD OF THE INVENTION

The present invention relates generally to cartography and moreparticularly relates to mapping of urban regions.

BACKGROUND OF THE INVENTION

The twentieth century, particularly, saw the exponential growth of urbanregions throughout the world, and in its latter half, the quantumdevelopment of suburban districts around the peripheries of cities,fuelled by expressways and the dominance of the automobile-basedsociety. This condition, in which the majority of North Americans, forexample, now live in suburbs with low rates of built density and humanactivity, is generally unable economically to sustain masstransportation. Residence, work, shopping and leisure are not only lowin density, and highly land consuming, but activities are generallysegregated and separate. In consequence, there is now widespread concernfor the effects of such dependence on the automobile—in air pollution,greatly increasing delays, in the increasing aggregate traveling thatdecreases the quality of peoples lives in costs, time and difficultiesin getting to jobs, and in many other respects.

A range of policies and practices have been promoted to deal with thissituation, developing forms of land use and transportation incombination, so as to conserve energy, minimize emissions of pollutants,encourage accessibility while minimizing mobility—for example, bydeveloping intensive activity centres around public transport nodes.Regions around the globe are involved in efforts to translate theseambitions into regional strategic development frameworks.

In general, as the world population becomes more concentrated in urbanregions, the quality of life in any given urban region is greatlyaffected by how well the urban region is equipped with infrastructure tosupport the needs of the local population. Urban planning is awell-known discipline that is used to plan how such infrastructure isadded, replaced and maintained. Urban planning also encompasses a numberof other issues as will occur to those of skill in the art.

At least in developed countries, most urban regions implement varyingdegrees of urban planning. The process is often heavily influenced bypolitical factors, as issues around taxation and property rights arenecessarily intertwined with the urban planning process. Recently inNorth America, there has been a trend towards “lean government”policies, wherein government-based centralized urban planning is largelyabandoned in favour of allowing the urban region to grow in alaissez-faire manner, on the belief that the free market is the bestdeterminer as to how the area should grow. Still other administrationsmay implement a more activist policies, involving a great deal ofcentralized planning, with the view that government controlled centralplanning is the most efficient way to serve the needs of the localpopulation. Of course, the approach for any given region usually liesbetween these extremes. Regardless of the chosen approach, one problemwith prior art urban mapping and data collection techniques is thatthere is little in the way of hard-data that can be analyzed to providean objective view as to how urban planning can be implemented mosteffectively.

The hard-data that exists today, which has been collected inconsistentlyacross a region, suggests that more data, and the right kind of data,could be extremely effective in urban planning. For example, as of 2003,it is known that the city of Toronto has a subway system that supportsitself largely out of the fare-box, with little reliance on governmentsubsidies. It is hypothesized that a major factor contributing to thisphenomenon is that there is a large population density that lives(“residential district”) adjacent to subway stations, and there is atleast one concentrated area in the downtown core where that populationworks (“employment district”) that is also adjacent to subway stations.A similar phenomenon can be observed in New York. The effort required togenerate a report to support this hypothesis, however, is enormous,complex, time-consuming and costly. As one approach, the effort couldinvolve collecting street maps and subway maps of Toronto, and thenconducting door-to-door surveys in both the residential and employmentdistricts to verify that people are actually using the subways tocommute to work. Finally, the data collected from the door-to-doorsurveys may then be correlated with the maps to ultimately arrive at areport with a conclusion that supports the hypothesis. However, it canbe noted that the report includes only a few sets of data points, anddoes not include other data that may influence whether or not simpledensities of residential districts and employment districts issufficient to support subway lines. Such a report also does not describethe structure of the built environment which dictates the densities.Further, such a report is not readily comparable with how other Urbanregions handle transport from residential districts to employmentdistricts, to provide an objective assessment as to which urban regionis best handling its transportation needs. More complex questions as tohow a particular urban region functions in relation to another willoccur to those of skill in the art, and the generation of reports toanswer such questions will face similar hurdles and complexities.

As previously mentioned, prior art urban maps are a very useful elementin the generation of the above-described type of report for urbanplanning exercises. Prior art urban maps principally identify physicalcharacteristics of transportation routes, and include identifiers likestreet names and station names on those maps. The maps may includeindications as to whether a particular area is more dominated byresidential, commercial or industrial activity, but little more. Ingeneral, such maps are very useful for navigating the urban region, butprovide limited information when attempting to generate complex reportsfor urban planning.

More recent urban maps of the prior art offer information that can beused for more than simply navigating the urban region. These maps aregenerated at least in part, using remotely sensed data obtained fromsatellites, air-planes and the like. Baltsavias, Emmanuel P. and A.Gruen. “Resolution Convergence: A comparison of aerial photos, LIDAR andIKONOS for monitoring cities” in Remotely Sensed Cities, edited byVictor Mesev, Taylor & Francis, London, 2003 (“Baltsavias”) is one priorart reference that discloses an example of such an urban map. Baltsaviasincludes a review and evaluation of the use of current high-resolutionremote sensing technologies including aerial/digital orthoimagery,Laser-Induced Detection and Ranging (“LIDAR”), IKONOS (4-meters perpixel colour and 1 meter per pixel black-and-white optical satelliteimagery) to extract geospatial information such as:

-   -   1) digital terrain models (“DTM”, an elevation model that is a        representation of the bare surface of the earth with natural and        manmade features removed.);    -   2) digital surface models (“DSM”, also referred to as a “first        surface” model in which man-made and natural features are        captured in the elevation model.); and,    -   3) an identification of urban objects such as buildings, roads,        vegetation, etc, and reconstruction of three-dimensional urban        objects such as buildings.        Baltsavias describes requirements for developing        three-dimensional city models and briefly describes two        commercial applications that have been developed, InJECT, a        product of INPHO GmbH, Stuggart, Germany and CyberCity Modeler        (CC-Modeler) marketed by CyberCity AG, Bellikon, Switzerland.        Baltsavias describes a prototype system, CyberCity Spatial        Information System (“CC-SIS”) which is an attempt to integrate        three dimensional city models with a relational database that        can be potentially linked to external Geographic Information        Systems (“GIS”) data. In order to identify objects, the user        manually identifies points onscreen, and only then will the        application automatically build topology that includes the        geometry needed to relate those points and identify an object.        The application requires the use of digital orthophotos which        are costly to acquire at the resolution that is necessary to        build the city model. Further, Baltsavias does not explain how        to derive building use or type and its relation to other        buildings in its immediate proximity or at the city-wide scale.        The application does not allow a user to assess how a region        functions or compares to other urban regions. In general,        Baltsavias is limited in how it offers to describe and visualize        an urban region's composition and functions.

Another example of increased urban map sophistication is found inBarnsley, Michael J., A. M. Steel, and S. Barr. “Determining urban landuse through an analysis of the spatial composition of buildingsidentified in LIDAR and multispectral image data,” in Remotely SensedCities, edited by Victor Mesev. Taylor & Francis, London, 2003.(“Barnsley”). Barnsley uses a combination of IKONOS at 4 meters perpixel colour satellite imagery and LIDAR (2 m) image data at 0.4 pointsampling density per square-meter, to extract the existence of buildingobjects from other surrounding objects, such as trees or paved roads.The results of the extraction were compared to base data to gageaccuracy of results. Four test areas are used where the predominant landuse is either residential or industrial. Given the limitations of thedata sets several thresholds were applied to the data to improve theresults. Barnsley develops a graph-based pattern recognition system toinfer land use by height and structural configuration. The technologyand techniques used in Barnsley to extract building objectssemi-automatically and to identify differences in morphologicalproperties of buildings and the structural composition of built formpatterns were successful in differentiating general land use types,(e.g. residential versus industrial), but there were problems inidentifying and characterizing unique patterns within these general landuse types, different residential and industrial patterns were not ableto be characterized given the measurement techniques used. In general,Barnsley does not teach how to classify and describe the unique builtform for different residential and industrial uses.

An example of an as-yet unfulfilled attempt to provide a moresophisticated urban map is found in Eguchi, Ronald, C. Huyck, B.Houshmand, D. Tralli, and M. Shinozuka. “A New Application of BuildingInventories using Synthetic Aperture Radar Technology.”, presented atthe 2nd Multi-Lateral Workshop on Development of Earthquake and TsunamiDisasters Mitigation Technologies and their Integration for theAsia-Pacific region. Mar. 1-2, 2000. Kobe, Japan. (“Eguchi”). UsingInterferometric Synthetic Aperture Radar (IFSAR) airborne technology,aerial photography and county tax assessment data, Eguchi attempts toidentify building types based on building footprint and height whichthey extract from the remotely sensed data and validate results usingcounty tax assessment data. The preliminary results of the techniquesused and future research plans are presented in Eguchi, laying thegroundwork to work towards a building inventory at a city-wide scalefrom which they can measure building density and development. Despitethe groundwork that has been laid, there is no indication of success orhow such success will be achieved.

Another example is Mesev, Victor. “Urban Land Use Reconstruction: ImagePattern Recognition from Address Point Information.”, presented at theInternational Archives of Photogrammetry, Remote Sensing and SpatialInformation Sciences Conference, Regensburg, Germany, 27-29 Jun., 2003.(“Mesev”). Mesev examines the use of address point data collected by theOrdnance Survey in the UK to examine spatial patterns of development inBristol UK. The address point data contains information on general landuse types, residential versus commercial, and Mesev attempts to identifydifferences between different areas of the same land use type, e.g.residential #1 and residential #2, based on a various spatialindices/techniques, i.e. density of points and nearest neighborhoodanalysis. This information from this spatial recognition system is usedto inform multispectral image classifications of urban regions. Mesevintroduces some preliminary results used on fine resolution aerialphotography provided by a company called Cities Revealed (TheGeoInformation Group, Telford House, Fulbourn, Cambridge, CB1 5HB,United Kingdom—http://www.crworld.co.uk). The remote sensed imagery forCities Revealed is quite costly to acquire for a large urban region. Thedata used for the pattern recognition is unique to the UK but notavailable for all regions, since the UK can rely so heavily on the UKOrdnance Survey. Likewise the spatial indices are not fully successfulon other urban land use classes such as commercial and industrial whereinformation on building characteristics would be more useful than justthe arrangement of buildings.

SUMMARY OF THE INVENTION

It is an object of the present invention to provide a novel method andsystem for mapping that obviates or mitigates at least one of theabove-identified disadvantages of the prior art.

An aspect of the invention provides a map of an urban region comprisinga first set of indicia representing physiographic characteristics ofsaid region and a second set of indicia representing a plurality ofdifferent types of built forms, and their locations, that are locatedwithin said region. The map also includes a third set of indiciarepresenting patterns of human activity associated with both saidphysiographic characteristics and said built forms.

Another aspect of the invention provides an apparatus that includesremote sensing equipment that is connected to a computer processor. Theremote sensing equipment gathers imaging data about an urban region. Thecomputer processor interprets the imaging data to generate a map of theurban region comprising representations that identify a first set ofindicia representing physiographic characteristics, a second set ofindicia representing different types of built forms, and a third set ofindicia representing patterns of human activity associated with both thephysiographic characteristics and the built forms. The map can alsoinclude a fourth set of indicia representing an intensity level that atleast one of the other types of indicia occurs.

BRIEF DESCRIPTION OF THE DRAWINGS

The present invention will now be explained, by way of example only,with reference to certain embodiments and the attached Figures in which:

FIG. 1 is a representation of a section of a geographic territorycontaining a number of urban regions;

FIG. 2 is a representation of an area within an urban region in theterritory of FIG. 1 being remotely sensed;

FIG. 3 is a representation of the remote sensing being performed in FIG.2 in greater detail;

FIG. 4 is a representation of the remote sensing being performed in FIG.3 in greater detail;

FIG. 5 is a representation of the data sensed in FIGS. 2-4 beinginputted into an apparatus for generating a map in accordance with anembodiment of the invention;

FIG. 6 is a representation of a database stored in the storage device inFIG. 5 that can be used to interpret raw data sensed in FIGS. 2-4;

FIG. 7 shows the objects in the database in FIG. 6 in greater detail;

FIG. 8 is a flowchart depicting a method of generating a map inaccordance with an embodiment of the invention;

FIG. 9 shows sensed data representing an area within a region that isbeing parsed in accordance with a performance of the method in FIG. 8;

FIG. 10 shows a small block within the area shown in FIG. 9;

FIG. 11 shows a map of the block in FIG. 10 that was generated using themethod of FIG. 8;

FIG. 12 shows the map of FIG. 11, generated using a variation on themethod of FIG. 8 to produce a different map format;

FIG. 13 shows a map in the same format as the map of FIG. 12, whereinthe map shows the area in FIG. 9;

FIG. 14 shows a map of the same format as the map of FIG. 13 expanded tothe regional level;

FIG. 15 shows an apparatus for generating a map in accordance withanother embodiment of the invention;

FIG. 16 shows a map of the area in FIG. 9 generated by the apparatus ofFIG. 15 and depicting the density of residence in the area;

FIG. 17 shows flowchart depicting a method of generating a map inaccordance with another embodiment of the invention; and,

FIG. 18 shows an exemplary graph that can be generated when performingthe method in FIG. 17.

DESCRIPTION OF THE INVENTION

FIG. 1 shows a territory 40 containing a plurality of urban regions 44.In the example in FIG. 1, territory 40 is a section of North Americaroughly bisected by the US-Canada border, but it is to be understoodthat this is merely an example of a territory to which the teachingsherein can apply. Thus, the urban regions 44 in territory 40 includevarious well-known urban regions, including Toronto, indicated at 44 ₁,Detroit indicated at 44 ₂, and New York at 44 ₃—other areas are simplyindicated by the reference 44. It should be understood that, in apresent embodiment, area 44 is not intended in its political sense, butrather to indicate urban regions in a geographic sense. Thus, an arealike Toronto 44 ₁ refers to the Greater Toronto Area, or the entire“Golden Horseshoe”, spanning the municipalities from Hamilton toBowmanville along the north shore of Lake Ontario. In like fashion,Detroit 44 ₂, and New York 44 ₃ refer to their respective greatermetropolitan areas.

FIG. 1 also shows two remote sensing devices 48, namely an airplane 48 ₁and a satellite 48 ₂ passing over territory 40. Devices 48 includeimaging equipment to enable device 48 to be operable to remotely sensedata associated with urban regions 44, according to a desired andappropriate remote sensing modality such as aerial photography,aerial/digital orthoimagery, LIDAR, IKONOS, RADAR. Other types ofdevices 48, and modalities respective thereto, will occur to those ofskill in the art.

FIG. 2 shows device 48 (i.e. airplane 48 ₁) remotely sensing datarespective to a particular area 52 within a region 44 (i.e. Toronto 44₁). In general, device 48 is operable to sense data associated with aplurality of areas within a particular region 44, thereby remotelysensing data that comprises the entire region 44. Thus, it is to beunderstood that area 52 is shown as an example for purposes ofexplaining various embodiments of the invention.

FIG. 3 shows area 52 in greater detail, and in a present embodiment,area 52 is sensed by device 48 as a photograph. Area 52 (and/or portionsthereof and/or other portions of region 44) can be characterized interms of a number of indicia, including physiographic forms, builtforms, activity patterns, and use intensity, and various degreesthereof. Physiographic forms includes the natural physical features ofarea 52, including landscape and physical objects such as terrain,trees, rivers, and streams. More specific terms ways of describingphysiographic forms can be found in Anderson, James R., E. E. Hardy, J.T. Roach, and R. E. Witmer, 1976. “A Land Use And Land CoverClassification System For Use With Remote Sensor Data.” GeologicalSurvey Professional Paper 964, the contents of which are incorporatedherein by reference. In contrast, built forms include anythingartificially constructed upon the physiographic forms, such as roads,houses, buildings, parks, parking lots, monuments, etc. (Table I,hereinbelow, provides a detailed list of potential built form types.)Activity patterns include the nature of the human activity/activitiesoccurring within area 52, and can include information about employment,residency, recreation, industry, commerce and/or combinations thereof.Finally, use intensity is a metric identifying the extent or amount of aparticular activity is occurring. Intensity can also include the degreeof a particular activity, or mix of activities, in order to describe thepossibility of a varying range to the activity indicium of theclassification scheme. Further details about these indicia will bediscussed in greater detail below.

FIG. 4 shows a small portion of area 52 in further detail, with device48 passing over, and sensing physiographic forms 56, in the form oftrees 56 ₁ and a stream 56 ₂, and built forms 60, in the form of houses60 ₁, an office tower 60 ₂, and an apartment building 60 ₃.

FIG. 5 shows the transfer of data 64 sensed by device 48 that includes aphotograph of area 52 being transferred from device 48 to an apparatus68 for mapping in accordance with an embodiment of the invention.Apparatus 68 is generally operable to interpret data 64 to generate amap of area 52 that is based on, at least in part, some or all of theabove-identified indicia. In the present embodiment, apparatus 68 is aserver, but can be a desktop computer, client, terminal, personaldigital assistant or any other computing device. Apparatus 68 comprisesa tower 72, connected to an output device 76 for presenting output to auser and one or more input devices 80 for receiving input from a user.In the present embodiment, output device 76 is a monitor, and inputdevices 80 include a keyboard 80 ₁ and a mouse 80 ₂. Other outputdevices and input devices will occur to those of skill in the art. Tower72 is also connected to a storage device 84, such as a hard-disc driveor redundant array of inexpensive discs (“RAID”), which containsreference data for use in interpreting data 64, further details of whichwill be provided below. Tower 72 typically houses at least one centralprocessing unit (“CPU”) coupled to random access memory via a bus. Inthe present embodiment, tower 72 also includes a network interface cardand connects to a network 88, which can be the intranet, interne or anyother type of network for interconnecting a plurality of computers, asdesired. Apparatus 68 can output maps generated by apparatus 68 tonetwork 88 and/or apparatus 68 can receive data, in addition to data 64,to be used to generate a map of area 52 that is based on, at least inpart, some or all of the above-identified indicia.

FIG. 6 shows a simplified representation of the kind of databases andtables that can be stored on storage device 84 to assist the CPU intower 72 with the interpretation of data 64. In FIG. 6, storage device84 stores a two-dimensional table 92. Table 92 includes built form data,comprised of a left column 96, labelled “Raw Data”, and a right column100, labelled “Interpretation”. Thus, each record in table 92 includes,in left column 96, an object 104 corresponding to raw data that may befound in remotely sensed data 64, and in right column 100, an object 108identifying a corresponding interpretation of object 104. Morespecifically, object 104 ₁ corresponds to a house, object 104 ₂corresponds to an office tower, and object 104 _(n) corresponds to anapartment building.

It is expected that the raw data found in data 64 will include a numberartifacts and other unique identifiers, and table 92 will includeinformation about such identifiers to provide CPU in tower 72 withadditional information to use when distinguishing between various typesof built forms found in data 64. FIG. 7 shows objects 104 in greaterdetail, to provide examples of the kinds of identifiers that can beassociated with each object in table 92. For example, it is to be notedthat each object 104 includes a shadow 112. Note that shadow 112 ₂ isthe longest, shadow 112 _(n) is shorter than shadow 112 ₂ and shadow 112₁ is shorter than shadow 112 _(n). Such shadow length as found in data64 can be used to infer the height of a given object 104, and thereforecan assist CPU in tower 72 with inferring the type of built form that isassociated with a given object found in data 64.

By determining relative heights of objects in data 64, the CPU in tower72 can make relative decisions about the appropriate interpretation tobe given to a particular object 104. In this example, a shadow 112 isused to determine the height of a given object, but it should beunderstood that more sophisticated means can be used to inferheight—such as through the use of LIDAR. Thus, when data 64 iscollected, it can be desired to use a combination of sensing modalities,i.e. photography and LIDAR, and to combine that sensed data to deriveeven further information and identification about objects in area 52.

It should now be understood that a variety of different identifiers canbe used in computing operations performed by the CPU in tower 72 tofurther assist in the distinguishing of various built forms found inarea 52. For example, the presence of two squares 116 on each end ofobject 104 _(n) are indicative of the presence of elevator shafts, andthe rectangular shape of object 104 _(n), in combination with thepresence of the elevator shafts and its shorter height than object 104 ₂can be used to determine that object 104 _(n) is an apartment building.As an additional example, object 104 ₁ includes a peak line 120 of itsroof, as further indication that object 104 ₁ is a house.

As another example of an identifier, close groupings of elements in data64 that resemble objects 104 ₁ can be used as a further indicator thatsuch an element is in fact a house 60 ₁. As still a further example ofan identifier, large open spaces detected around a given element foundin data 64 can be indicative of parking lots, thereby eliminating thelikelihood that a given element in data 64 is actually a house 60 ₁.

As an additional identifier, in certain geographic regions, particularlyin North America, there is a limited number of built form types thatrecur. Due to this limited number, probability formulations can be used,in addition to the identifiers such as the identifiers listed above (orsuch other identifiers as may be determined to be useful from time totime), to improve the likelihood of an accurate determination of aparticular built form type. Table I shows a list of such built formtypes and identifiers that can accompany each type that can be used indatabases on storage device 84 (such as table 92) and in conjunctionwith software executing on tower 72 to actually distinguish certainbuilt form types from others.

TABLE I Built form types Activity Type (Inferred From Built Form BuiltForm Type Description And Identifiers Identification) Detached HouseSingle family dwelling, Residential commonly found in sub-urbandistricts (residential neighbourhoods). Semi-Detached One to fourstoreys, each a Residential House discreet building on a fenced parcel.Usually ridge-roofed Row House Or As above, but with one wall,Residential Town House shared with another house. Mid-rise Apartment Abuilding consisting of joined Residential Building single familydwellings, as above. One to four storeys with shared walls. High-RiseMultiple family apartment Residential Apartment building of five toeight floors, Building often located (in north america) along mainstreets, normally with an elevator core and predictable dimensions. LowTo Mid-Rise Free-standing buildings of 2 to 8 Office/ Office Buildingstoreys, most commonly in Administration suburban locations and mainstreets. Distinguished usually by entrance and surface parking patternsaround them. High-Rise Office Free-standing building of 8 or Office/Tower more storeys, identified Administration particularly by platedimensions and shape. Main Street Shop 2 to 4 storey buildings withparty Retail Building walls, characterized by relatively narrow streetfrontages, composing shopping streets. Strip Mall Single-storeybuildings arranged Retail along or around surface parking lots. ShoppingMall A building composed of larger Retail and smaller elements(department stores and shops, surrounded by surface parking and withspecific truck loading patterns. Big Box Store Free-standing retailstore of Retail characteristic dimensions, with surface parkingadjacent. Factory Large-plate, deep-space building Manufacturing of oneor two storeys, with characteristic truck access and loading patterns.Road/Highway Long, continuous pathway that Transportation separatesother discrete built form types. Railway Long, continuous pathway thatTransportation separates other discrete built form types.

Thus, once tower 72 receives data 64, it can perform a progressive scan(or other suitable analytical technique) thereof, parsing elements foundin the data 64 representing area 52, and compare those parsed elementswith the information in table 92, particularly, the raw data left column86 to ultimately identify the type of built form at that particularlocation in area 52. More particularly, once data 64 is parsed andobjects therein are isolated, CPU in tower 72 can detect the presenceand location of houses 60 ₁, office towers 60 ₂ and apartment building60 ₃. In general, those of skill in the art will recognize that thetasks being performed by CPU in tower 72 can be based on knowntechniques found in commercially available software that are currentlyapplied to determining types of terrain and modelling of buildings. Seefor example http://www.tec.army.mil/TD/tvd/survey/index.html of the USArmy Corps of Engineers. Those of skill in the art will now understandthat such known techniques provide operations and software proceduresfor recognizing the presence, and showing the configuration of, variousphysiographic forms and built forms, but are not generally suitable, intheir current form, to perform the task of identifying different typesof built forms. (i.e. In the military context, the purpose of gatheringsuch information is for gaining battlefield advantage to invade ordefend an urban region, and not for the purpose of planning improvementsto the region.)

Referring now to FIG. 8, a method for generating a map is indicatedgenerally at 300. In order to assist in the explanation of the method,it will be assumed that method 300 is operated using apparatus 68 andthe above-described aspects relating thereto. Furthermore, the followingdiscussion of method 300 lead to further understanding of apparatus 68.(However, it is to be understood that apparatus 68 and/or method 300 canbe varied, and need not work exactly as discussed herein in conjunctionwith each other, and that such variations are within the scope of thepresent invention.)

Beginning first at step 310, remotely sensed data of an urban region isreceived. This step is essentially performed as previously described,with remote sensing device 48 passing over a given urban region 44 and,with its imaging technology activated, the device 48 gathers data, suchas data 64, of a particular region 44. The data 64 is then transferredto tower 72 of apparatus 68 using appropriate network interfaces—such asvia wireless directly from the device 48, or by means of a physicalmedia that is removed from device 48 and inserted into a reading deviceon tower 72, or by any other means as desired.

Next, at step 320, the remotely sensed data is parsed into objects withlocation coordinates. This step can be done according to any known ordesired technique of analyzing data 64 to extract individual objects,and assign coordinates thereto, as will occur to those of skill in theart. For example, FIG. 9 shows area 52 in data 64 being divided into alogical grid 124, with an (X,Y) coordinate system, and with an origin128 at the coordinates (0,0). FIG. 9 also shows four squares in grid 124highlighted as block 132. Block 132 begins at coordinate (6, 2) on grid124. To help further explain method 300, block 132 will be discussed ingreater detail as an example of how method 300 can process data 64.

FIG. 10 shows block 132 of data 64 in greater detail. Thus, when step320 is performed on block 132, image processing performed on that block132 leads to the identification of objects 136 ₁, 136 ₂, 136 ₃ . . . 136₆ as labelled on FIG. 10. Note that, the manner in which objects 136 ₁,136 ₂, 136 ₃ . . . 136 ₆ are shown in block 132 is to be representativeof the appearance of objects 136 ₁, 136 ₂, 136 ₃ . . . 136 ₆ of suchobjects as raw collected data by device 48, as packaged within data 64.Thus objects 136 ₁, 136 ₂, 136 ₃ . . . 136 ₆ include a number ofartifacts and other identifiers, but objects 136 ₁, 136 ₂, 136 ₃ . . .136 ₆ are otherwise currently unidentified. Thus, also when step 320 isperformed on block 132, a database is created that lists those objectsand their locations. Table II shows a list of location coordinates thatwould be created during the performance of step 320 on block 132.

TABLE II Objects and locations in Block 132 Location Coordinates ObjectNumber (X, Y) Object 136₁ (6.3, 3.3) Object 136₂ (7.1, 3.6) Object 136₃(6.4, 2.8) Object 136₄ (7.7, 3.2) Object 136₅ (7.3, 2.9) Object 136₆(7.5, 2.3)

Referring again FIG. 8, method 300 advances from step 320 to step 325,at which point the objects parsed at step 320 are compared with a set ofexpected built forms. Thus, having identified objects 136 ₁, 136 ₂, 136₃ . . . 136 ₆, each one is then compared with an expected set of builtforms stored in device 84, and in particular, in table 92. Using table92 and the aforementioned interpretation techniques (or such otherinterpretation techniques as may be desired), an interpretation of theraw data associated with each object 136 can be obtained. During such acomparison, it will be determined that object 136 ₁ resembles object 104₂; objects 136 ₂ resembles object 104 _(n); and objects objects 136 ₃,136 ₄, 136 ₅ and 136 ₆ resemble object 104 ₁.

At step 330, the type of built forms of each object is determined. Thus,using the results of the comparison at step 325, tower 72 makes adetermination as to the identity of each of the objects 136 identifiedat step 320, and adds to the information in Table II to produce a newtable, of the form of Table III, that includes the built form type ofthat particular object.

TABLE III Built form type of each object in block 132 LocationCoordinates Built form Object Number (X, Y) Type Object 136₁ (6.3, 3.3)Office Tower Object 136₂ (7.1, 3.6) Apartment Building Object 136₃ (6.4,2.8) House Object 136₄ (7.7, 3.2) House Object 136₅ (7.3, 2.9) HouseObject 136₆ (7.5, 2.3) House

At step 335, a built form map is generated based on the results of theperformance of steps 320-330. Thus, tower 72 uses the information inTable III to redraw block 132. As shown in FIG. 11, tower 72 therebygenerates block 132 a from block 132 and Table III, and outputs block132 a onto output device 76. (It should be understood that block 132 acan also be saved on storage device 84, or sent to another computingdevice on network 88, or output in other ways.) Block 132 a thusrepresents the built forms in area 52 in iconographic form, and providesa legend as to the built form type of each icon present in block 132 a.In a similar manner, tower 72 can thus present all of area 52, and/orall of region 44 on display 76. In a present embodiment, it iscontemplated that a full range of navigational tools are provided, toallow a user to use input devices 80 to move around area 52 (or region44), and to zoom in or zoom out as desired.

As additional step to step 335, or as a variation to step 335, block 132can be generated in the form shown in FIG. 12, indicated as block 132 b,wherein a particular square bounded by a set of coordinates on grid 124is marked in accordance with the most prevalent type of building formfound within that particular square.

The methodology used to generate the map in FIG. 12 can also be appliedto generation of maps of area 52 (in the form shown in FIG. 13 as area52 a) and/or the entirety of region 44 (in the form shown in FIG. 14 asregion 44 ₁ a. In FIG. 13, area 52 has been rendered on output device 76into area 52 a, and is divided into four precincts 136 ₁, 136 ₂, . . .136 ₄. Each precinct 136 is shaded according to the type of built formthat is predominant in that precinct 136. Thus, precinct 136 ₁, is shownto consist predominantly of office towers 60 ₂; precinct 136 ₂ is shownto consist predominantly of apartment buildings 60 ₃; while precincts136 ₃, and 136 ₄ are shown to consist predominantly of houses 60 ₁. Itis to be understood that other built form precinct types can beincluded, such as hybrids of office towers 60 ₂ and houses 60 ₁, where agiven precinct consists of more than one predominant type of built form.It is also to be understood that the criteria used to determine whereone precinct 136 begins, and another ends, is not particularly limited,and can be based on any number of factors such as user selection,political boundaries, physiography, transportation routes, combinationsthereof, and/or can be based on more “fuzzy” types of logic where tower72 is configured to create precincts according to groupings of squaresin area 52 that are characterized by a predominance of a particularbuilt form types. It should be understood that the particular shapes ofprecincts are thus not limited, and such shapes will depend on thecriteria used to define a precinct. As an example, in a city such asToronto, Danforth Avenue exhibits common indicia along the stretch knownas “Greektown”—this oblong stretch could be selected as a criteria for aparticular precinct. So too, any area where a street exhibits commonindicia such that it is desirable or logical to define that street as aprecinct. It is also contemplated that new maps can be quickly generatedbased on user inputted changes to such criteria.

It will now be understood that where a map of the type shown in FIG. 13is generated for an entire region 44, comparisons of precinctscharacterized by predominant built form types, and distributionsthereof, between different regions 44 can be readily compared. Forexample, where a map of the type in FIG. 14 is created for Toronto 44 ₁,and another for New York 44 ₃, (not shown) a comparison can be made ofthe predominant built form types and their distribution throughout eachrespective region 44. It should now also be understood that the examplemap in FIG. 14 includes a broad range of built form types based on thelist of built form types shown in Table I. It should be noted, however,that the list in Table I is non-exhaustive, and that other built formswill now occur to those of skill in the art. For example, asemi-permanent, single detached, trailer is an additional type of builtform not listed in Table I.

The maps shown in FIGS. 11-14 are primarily directed to built form. Inother embodiments of the invention, however, maps can be generated thatinclude information in addition to built form. Such maps include otherindicia that can used to be characterize a particular region, includingphysiographic forms, activity patterns, and use intensity. The additionof physiographic forms is relatively straightforward using existingremote sensing and mapping techniques. Examples of existing commercialpackages that can be used as part of performing this addition ofphysiographic forms include ERDAS Imagine (from Leica Geosystems GIS &Mapping, LLC, Worldwide Headquarters, 2801 Buford Highway, N.E.,Atlanta, Ga. 30329-2137 USA) and PCI Geomatics (from 50 West WilmotStreet, Richmond Hill, Ontario Canada, L4B 1M5) for image processing.The commercial packages from ESRI ArcGIS (from ESRI, 380 New YorkStreet, Redlands, Calif. 92373-8100, USA) and Mapinfo (from MapInfo, 26Wellington Street East, Suite 500, Toronto, ON M5E 1S2) can be used formapping.

In the previous embodiment, a certain degree of activity pattern wasinferable due to the process of recognizing the built form types—i.e.that houses and apartments indicate an activity of “residence”, whileoffice towers indicate an activity pattern of “employment”. However, inother embodiments, activity patterns and/or use intensity is added usinggeospatial and/or demographic data corresponding to the region beingmapped. Geospatial data can include information that identifies thegeographic location and characteristics of natural or constructedfeatures and boundaries on the earth. Geospatial data information may bederived from, among other things, remote sensing, mapping, and surveyingtechnologies. Demographic data which can be considered a subset ofgeospatial data, and can include statistics relating births, deaths,ages, incomes etc. that illustrate the conditions of life in a givenregion 44.

As an example of the foregoing, in FIG. 15 demographic data 140 is inputinto tower 72 in conjunction with data 64 to be used in the generationof an enhanced map. As used herein, the sources of demographic data 140can be multi-fold, to include data that is collected and maintained bygovernment organizations, such as census data, taxation data, landregistry data, employment surveys, and to include data that is collectedand maintained by non-government organizations. Thus, the means by whichdemographic data 140 is actually inputted into tower 72 will depend onthe form in which it currently exists, and with appropriateconsideration to privacy laws. Thus, as tower 72 receives bothdemographic data 140 and remote sensed data 64, tower 72 will includefurther functionality to correlate the physical areas in region 44and/or area 52 that correlate with the demographic data 140 beingcollected. (It should be noted that for the sake of privacy,readily-available census data is often aggregated to a spatial unit,e.g. census tract or enumeration area, rather than by address. In orderto correlate it with the built form types, the data can be desegregatedand related to the individual buildings.)

Thus, one significant source of demographic data 140 that can be used todetermine activity patterns and/or intensity of use within region 44,area 52, block 132, or any given built form therein is census data.Census data that includes addresses can be correlated to the built formsdetected using method 300. Census data can be used to determine, forexample, how many individuals reside in the house identified as object136 ₃ in FIG. 10. In this manner, the density of the population residingwithin any precinct 136 can be determined. The precinct 136 can then beiconographically represented as a residential precinct, and in a mannerthat indicates the actual density of people living in that precinct. Anexample of a map outputted using this data is shown in FIG. 16. Precinct136 a ₁ is indicated to have zero to ten persons per square meterresiding in that precinct 136 a ₁. Precinct 136 a ₂ is indicated to havegreater than 100 persons per square meter residing in that precinct 136a ₂. Precincts 136 a ₃ and 136 a ₄ is indicated to have ten toone-hundred persons per square meter residing in those precincts. Itshould now be understood that the type of map in FIG. 16 can be expandedto the regional scale, and again readily permit comparisons betweendifferent regions 44 for which maps of that type are generated. (Notethat while the units of persons per square meter is chosen, and suitableunit can be used, such as persons per hectare, etc.)

As an additional comment however, while the map in FIG. 16 is describedas having been generated using demographic data 140, it can also bepossible to infer average levels of occupancy based on the determinedbuilt form type from Table I, and use that inferred level to develop themap in FIG. 16.

By the same token, other types of demographic data 140 can be used todetermine the number of employees working at the office tower identifiedas object 136 ₁ in FIG. 10. Other types of activity patterns, intensityof uses and other indicia to create specific precincts will now occur tothose of skill in the art. Table IV below, however, provides anexemplary list of activity patterns, and metrics for intensity of use toaccompany those patterns that can be used to generate maps using theteachings herein.

TABLE IV Activity Patterns and Use Intensity Activity Use IntensityMetric Residence Average number of persons per square meter residing inprecinct Average number of persons residing per cubic meter in precinctEmployment Average number of persons per square meter employed inprecinct Retail Number of stores per square meter in precinct TransportNumber of persons using transport through corridor per day

In general, it should now be understood that maps of regions 44 can begenerated using the teachings herein in an automated and relativelyefficient manner. Further, it should be understood that such maps, atthe regional level, can be generated to include a plurality ofprecincts, where each of those precincts is uniquely identifiableaccording to a set of trends or commonalties between a set of indiciathat can be used to characterize an urban region. Such precinct maps ofregions 44 can be used for urban planning purposes, to compare withother urban regions, and/or in their own right, to determine how best toadd, replace and/or maintain infrastructure in an urban region. Precinctmaps can be generated according to a specific urban planning project orquestion. For example, if it is to be determined whether a particularregion can support a new subway line, then a precinct map can begenerated that identifies residential precincts and employmentprecincts, with the view to choosing a path for the subway line betweensuch precincts provided that such precincts appear to have populationsthat are able to support the new subway line. Such precinct maps canalso be used for a variety of other planning purposes, includingairports, cell phone deployments, new highway construction, sewage andwater treatment facilities, power line and supply requirements and thelike. Other types of precinct maps for other types of planning purposeswill now occur to those of skill in the art.

Referring now to FIG. 17, a method for generating a map is indicatedgenerally at 500. Method 500 can be operated using apparatus 68 and theabove-described aspects relating thereto. It is to be understood thatapparatus 68 and/or method 500 can be varied, and need not work exactlyas discussed herein in conjunction with each other. At step 510,geographic data is received. The data can be received as previouslydescribed, based on satellite images, or it can be received as existingGIS data. At step 515, the received data is parsed. The data is parsedusing any technique that corresponds with the desired types of indiciato be presented in the final map to be generated. One way to parse thedata is as previously described with method 300, however, any types ofprocessing techniques, including known image processing and GISprocessing techniques can be used. Next, at step 520, indicia aregenerated and associated with their respective geospatial location inthe region of geographic data that was received at step 510. Any type ofindicium, or indicia or combinations thereof, can be generated,including built form, activity pattern, intensity of use etc. At step525, precinct boundaries in the region of geographic data received atstep 510 are defined using any desired criteria, such as the criteriapreviously described. At step 530, the indicia generated at step 520 areaggregated and overlayed onto the defined precincts boundaries togenerate a map of the region comprised of precincts that are identifiedaccording to the particular chosen set of indicia.

It is to be reiterated that the criteria or other means used to define aprecinct are not particularly limited. For example, Tables V-VII show anexample of measurements that can be could be generated by apparatus 68,and/or by method 500 for an exemplary precinct on area 52, called“Precinct 1”. Precinct “1”, may, for example, appear in a map such asthe type shown in FIG. 14. Table V relates to intensity measurements ofphysiography; Table VI relates to intensity measurements of built form;while Table VII relates to intensity measurements of activity. Thevalues associated with each field in the respective table reflect anintensity level, expressed in terms of percentage.

TABLE V Physiography and Intensity For Precint “1” Soil Tree Water Rock70% 10% 10% 10%

TABLE VI Built Form and Intensity For Precint “1” House Office TowerApartment Road/Highway 60% 0% 15% 25%

TABLE VII Activity and Intensity For Precint “1” Residential CommercialResidential Road Major Road 80% 0% 10% 10%

The data gathered in Tables when tabulated by apparatus 68, can resultin a graph of the type shown in FIG. 18, which is characterized as aprecinct of type “A”. Such characterizing of Precinct “1” as of beingtype “A” can be based on certain threshold percentages for eachtype/category of indicia and associated intensity. An example of suchthreshold values is shown in Table VIII.

TABLE VIII Threshold values for Precincts of type “A” Physiography SoilTree Water Rock Minimum 60%, Minimum 0% Minimum 0% Minimum 0% Maximum80% Maximum 10% Maximum 10% Maximum 10% Built Form House Office TowerApartment Road/Highway Minimum 50%, Minimum 0% Minimum 0% Minimum 15%Maximum 90% Maximum 10% Maximum 20% Maximum 40% Activity ResidentialCommercial Residential Road Major Road Minimum 70% Minimum 0% Minimum10% Minimum 0% Maximum 100% Maximum 10% Maximum 20% Maximum 10%

Thus, using the threshold values for a precinct of type “A”, (and/or aplurality of different precinct types) maps of different urban regionscan be generated to locate where there are common precincts of type “A”.Other uses for obtaining maps that identify precincts will now occur tothose of skill in the art. By the same token, it will now occur to thoseof skill in the art that any number and combinations of different typesof indicia can be used. Furthermore, while Tables V-VIII all refer topercentages of intensity, it should also be understood that Tables canmerely look for the presence or absence of a particular type of indicia.

While only specific combinations of the various features and componentsof the present invention have been discussed herein, it will be apparentto those of skill in the art that desired subsets of the disclosedfeatures and components and/or alternative combinations of thesefeatures and components can be utilized, as desired. For example, othermeans of remotely sensing data can be used—e.g. electronic surveyconducted by internet, involving the distribution of a survey toindividual subscribers who own a particular building within the regionbeing surveyed.

It should now be apparent to those of skill in the art that the presentinvention provides a novel Geographic Information System (“GIS”). It isalso to be understood that method 300 is but one particular way ofinterpreting remotely sensed data to generate the types of maps in FIGS.11, 12, 13, 14 and 16 and variations and/or combinations thereof, andthat additional methodologies can be employed, as desired. For example,another approach is to first use geospatial data to segment or parseareas in the remotely sensed data into “manageable” units that exhibitsimilar characteristics. Next, census tracts are located in the regionthat have a high population density by analyzing that variable for theentire region using an off-the-shelf GIS. Next, if it is assumed thatthat high population densities are the result of a high built formdensity, e.g. high-rise tower or slab apartments, the RS data for thosesame census tracts can be isolated and examined. The derived built formcan then be verified using other imagery data (e.g. aerial photography)and presented using metrics that characterize a particular built form,i.e. high-rise tower or slab apartments. In this instance geospatialdata informs the remotely sensed data and is usable in the assumptionsabout characteristics of built form. In general, any geospatial featureand tabular data that can be obtained and used to infer built form canbe employed in other embodiments of the invention.

Further, while the built form maps of FIGS. 13 and 14 are one type ofoutput that can be generated using the present invention, it should nowalso be understood that determined built forms can also be used to infercertain activities, and/or intensities thereof, and/or other indiciaused to generate other types precinct maps for region 44.

In a further embodiment of the invention, maps of type shown in FIGS.11, 12, 13, 14 and 16, and other maps showing other indicia, can begenerated for a given period of time, and then “played back” to theviewer to show an animation of change over time of a particular region,or portion thereof.

Another particular embodiment of the present invention is thestandardization of measurements used to create precincts for multipleregions 44, so that ready comparisons can be made between differentregions 44. The measurements used to identify any particular precinctcan be based on any one or more of the indicia of physiographics, builtform, activity patterns, etc. and/or intensities and/or combinationsthereof, in conjunction with area, volume or other geographic metrics ofa particular region. For example, a measurement can include a ratio ofone type of an activity pattern to another type of activity pattern fora give area.

The teachings herein can have a broad range of applications, inparticular for use in urban planning and commercial applications. Forexample:

-   -   1. Standardized maps can be provided of the indicia associated        with different regions. These can be useful to urban specialists        and to the general public, in providing an understanding.    -   2. Comparisons between these regions can be readily performed        due to the standardized approach to create such maps.    -   3. Growth patterns of a particular region can be recognized        through the identification at regular intervals of the extent        and classification of new precincts and of changes in        established areas (with regard to built form, activities and        intensities).    -   4. Strategic investment decisions can be made for an urban        regions—in office, retail or residential development, or in        land, or for house or other built form purchases    -   5. Measuring the densities of development, both gross and net,        and in particular for measuring new development.    -   6. Identification of opportunities across a region for urban        intensification can be performed. For example, by identifying        vacant or underused lands around subway or light-rail stations.    -   7. As a means of assessing effects of new investments in        infrastructure (e.g. a rail line) or a sectoral investment (such        as a “big box” retail centre).    -   8. At the urban regional level, the embodiments herein can be        used as a basis and tool for constructing consistent,        comprehensive and sufficiently informative regional growth plans        for infrastructural investments (public transport, roads,        sewerage, water, etc.) and the necessary accompanying strategies        for the deployment of buildings and activities. An example of        the importance of this understanding is that the United States        government has recently begun to require of all urban regions        that they have regional strategies that promote increased        reliance on public transportation if they are to receive        allocations from the Highway Fund for capital transportation        purposes. This requires a region to obtain regional        understandings of its component precincts, activities and        movement patterns, and the present and anticipated intensities.        Thus, the teachings herein can be incorporated into broader        methodologies used to perform actual urban planning and as part        of formulas used to calculate government grant allocations.

The present invention provides a novel system and method for mapping.The maps generated according to the teachings herein provide frameworksto understand, at the regional scale, the existing patterns and trendsof built form and activities, and their intensity; and the patterns ofcommunications. Since, in these respects, urban regions vary greatly,prior art technique do not allow for ready comparisons of differenturban regions. For example, the Ranstadt region (composed of Amsterdam,Rotterdam, the Hague and other cities) is poly-nuclear. The Londonregion is highly concentric. The Pearl River Delta (probably the world'slargest urban region) tends to be a carpet of highly mixed activity,with several highly compact and intensive nodes (Hong Kong, Shenzhen,Guangzhou, Zhuhai). Again, Toronto has an unusually compact centre andlow-density suburban periphery, a pattern that appears to be in theprocess of reinforcement with very low density exurban extensions and agreat wave of central urban intensification. Toronto, like virtually allNorth American urban regions has, in the past four decades, experiencedan explosion of suburban office space, most of it located in a largenumber of small and moderately-sized low density clusters along majorhighways and freeways. In North American urban regions this kind ofoffice sprawl now constitutes, more or less, half of the regional officespace. The present invention provides a novel system and method forgenerating maps to understand the aforementioned conditions andpatterns. Maps generated using the teachings herein can be provided thatallow ready comparisons between different regions, on a consistent,comprehensive, efficient and/or low cost basis. This is generally notpossible using prior art mapping techniques of in urban regions, nor isit possible to provide a level of information that provides a ready andproper basis for land use/transport policy and program formulation.

The above-described embodiments of the invention are intended to beexamples of the present invention and alterations and modifications maybe effected thereto, by those of skill in the art, without departingfrom the scope of the invention which is defined solely by the claimsappended hereto.

1-39. (canceled)
 40. A computer generated map of an urban regioncomprising: a first set of indicia representing physiographiccharacteristics of said region; a second set of indicia representing aplurality of different types of built forms, and their locations, thatare located within said region; and a third set of indicia representingpatterns of human activity associated with both said physiographiccharacteristics and said built forms.
 41. The computer generated map ofclaim 40 further comprising a fourth set of indicia representing anintensity level associated with at least one of said sets of indiciarepresenting a degree to which said indicia occurs.
 42. A computergenerated map of an urban region comprising representations thatidentify at least two different precincts, each of said precinctscomprising a first set of indicia representing physiographiccharacteristics of said region; a second set of indicia representing atleast three different types of built forms, and their locations, thatare located within said region; a third set of indicia representingpatterns of human activity associated with both said physiographiccharacteristics and said built forms.
 43. The computer generated map ofclaim 42 further comprising a fourth set of indicia representing anintensity level that at least one of said sets of indicia occurs andwherein said at least a portion of said sets of indicia respective toeach of said precincts are different.
 44. A computer-implemented methodof generating a map comprising: receiving, in at least one processingunit, a first set of data of an urban region representing physiographiccharacteristics; receiving, in said at least one processing unit, asecond set of data of said urban region representing built forms andtheir locations; receiving, in said at least one processing unit, athird set of data of said urban region representing patterns of humanactivity associated with both said physiographic characteristics andsaid built forms; determining, using said at least one processing unit,different types of entities for each said set of data; and generatinguser output containing indicia respective to each set of data thatcorresponds to said determined entities.
 45. The method according toclaim 44 wherein said user output is in a graphical form that includes aplurality of precincts, each of said precincts formed based on areaswithin said region that have a predefined set of common indicia.
 46. Themethod of claim 44 further comprising associating an intensity levelassociated with each set of indicia.
 47. The method according to claim44 wherein said first set of data and said second set of data areobtained from remotely sensed data sources.
 48. The method according toclaim 44 wherein said third set of data is obtained from census data.49. An apparatus for generating a map comprising the steps of: at leastone input device for receiving a first set of data of an urban regionrepresenting physiographic characteristics, a second set of data of saidurban region representing built forms and their locations and a thirdset of data of said urban region representing patterns of human activityassociated with both said physiographic characteristics and said builtforms; a microprocessor connected to said input device operable todetermine different types of entities for each said set of data; and auser output device connected to said microprocessor for presentingindicia respective to each set of data that corresponds to saidentities.
 50. The apparatus of claim 49 wherein said indicia ispresented in a graphical form that includes a plurality of precincts,each of said precincts defined by areas within said region that have apredefined set of common indicia.
 51. The apparatus of claim 50 furthercomprising the step of associating an intensity level associated witheach set of indicia.
 52. The apparatus according to claim 50 whereinsaid first set of data and said second set of data are obtained fromremotely sensed data sources.
 53. The apparatus according to claim 50wherein said third set of data is obtained from census data.